The present invention relates to a blind equalization system for use in a receiver (e.g., a modem) in a digital communication system, and, more particularly, to a method for adaptively controlling an on-and-off operation of a decision directed algorithm.
In digital communication systems, a transmitter inserts a predetermined training sequence into a transmission signal at a certain interval of the transmission signal, and transmits the inserted signal to a receiver. The receiver, on the other hand, detects and recognizes this training sequence, thereby discriminating channel characteristics of the transmitted signal. This allows the receiver to perform signal equalization with respect to the sequentially received signal according to an equalization coefficient which is determined by the discriminated channel characteristics. However, the transmitter may not transmit the transmission signal together with the training sequence. In this case, the receiver does not know the pattern and state of the received signal. Thus, a blind equalization method capable of updating the coefficients of an equalizer only with the received signal is employed.
The blind equalization method uses algorithms such as a Godard algorithm, a stop-and-go algorithm (SGA), and a decision-directed algorithm (DDA), as examples.
The Godard algorithm performs excellent compensation operation with respect to channel distortion irrespective of any phase error. However, its convergency rate is slow, and thereafter dispersion on the constellation of the data symbol occurs. Thus, for fine tuning operations, it is necessary to convert the Godard algorithm into the DDA. On the other hand, the DDA is not converged until the channel distortion of the received signal is removed to a degree. Thus, when equalization is performed using the DDA at the state where the Godard algorithm has not been firstly performed, convergency of the equalization coefficient cannot be expected. Thus, to perform more effective equalization, conversion of the Godard algorithm into the DDA should be timely accomplished. Moreover, if the SGA is used for on-and-off control of the DDA, an equalizer having better performance than that using only the Godard algorithm and the DDA can be obtained.
With reference to FIGS. 1 and 2, a conventional blind equalization system for the on-and-off controlling of the DDA using the SGA will be described.
FIG. 1 is a block diagram showing part of a general blind equalization system. In a general blind equalization system as shown in FIG. 1, an SGA executer 14 supplies to a blind equalizer 11 an equalization coefficient C.sub.n which is updated by the following equations (1) and (2) . EQU C.sub.n+1,R =C.sub.n,R -.alpha.(f.sub.n,R .multidot.e.sub.n,R .multidot.Y.sub.n,R +f.sub.n,I .multidot.e.sub.n,I .multidot.Y.sub.n,I) (1) EQU C.sub.n+1,I =C.sub.n,I +.alpha.(f.sub.n,R .multidot.e.sub.n,R .multidot.Y.sub.n,I -f.sub.n,I .multidot.e.sub.n,I .multidot.Y.sub.n,R) (2)
The first equation (1) represents the real part of the equalization coefficient C.sub.n, and the second equation (2) represents the imaginary part thereof. Here, C. is a coefficient vector of an equalizer, Y.sub.n is an input vector of the equalizer, e is an error vector, and .alpha. is a step size of the SGA. The error vector e is represented by the following equation. ##EQU1##
Here, Z.sub.n is output data of the blind equalizer 11, and a.sub.n is decision point data which is output from a decision device 15.
FIG. 2 illustrates the concept of the error vector e.sub.n. The dotted lines of FIG. 2 represent decision boundary lines for determining signals which are quadrature amplitude modulated and then transmitted. The SGA executer 14 uses the DDA and determines data a.sub.n of the decision point which is closest to the equalized data Z.sub.n as the transmitted signal, and updates the equalization coefficients of the blind equalizer 11 according to the error vector e.sub.n, which is the difference between the decision point data a.sub.n and the equalized data Z.sub.n.
The most important reason why the DDA is not converged is due to decision errors corresponding to the above-described error vector e.sub.n. If a transmission path is not a multiplexed path and only small noise exists in the transmission signal without having a phase error, the error vector e.sub.n is nearly the same as the actual error which is produced in the transmission signal. Accordingly, the DDA can be better converged. However, if the actual signal transmission channel is a multiplexed path, and the received signal is wrongly restored by the noise and the non-linear filtering, the equalized signal comes out of the decision boundary and is located in a different place. Thus, the error vector e.sub.n becomes different from the actual error. Taking 32 quadrature amplitude modulation (QAM) as an example, the DDA maps the equalized data with one of the 32 points which are closest to the equalized data. Accordingly, the probability of the decision error becomes high. As a result, the DDA cannot converge the equalization coefficient into an optimal value.
Therefore, the SGA executer 14 does not use the error vector e.sub.n of equation (3) which is produced for the received data by the DDA, but, rather, controls the DDA using equations (4) and (5). ##EQU2##
The data e.sub.n in equation (4) is the actual error estimation data which is called a Sato-like error obtained by subtracting a sign of the data (sgn Z.sub.n) multiplied by a predetermined coefficient .beta..sub.n from the output data Z.sub.n of the equalizer 11. The SGA executer 14 performs the DDA when the sign of the error vector e.sub.n coincides with the sign of the actual error estimation data e.sub.n in equation (5). When the signs do not coincide with each other, the DDA is not performed. The distribution state of the actual error estimation data e.sub.n is shown in FIG. 3.
FIG. 3 is a conceptual diagram illustrating a conventional decision area dividing method. In FIG. 3, the horizontal axis is an in-phase (I) axis, and the vertical axis is a quadrature (Q) axis. In FIG. 3, a solid line represents a decision boundary for discerning each decision area. In each decision area, sign pairs "(+,+), (+,-), . . . " of the actual error estimation data e.sub.n include "(a sign of the real part, a sign of the imaginary part)," respectively. Here, the size of the decision boundary is adjusted by the predetermined coefficient .beta..sub.n in equation (4). The value of the coefficient .beta..sub.n is experimentally obtained according to the characteristics of the equalization system. If the I-axis and the Q-axis are divided into four areas, respectively, using the decision boundary, the probability of the decision error is decreased. However, considering the two-dimensional plane which is formed of the I-axis and Q-axis, the symbol constellation is divided into sixteen decision areas by the I-axis and Q-axis which are divided into the four areas as shown in FIG. 3.